Association of Tumor-Localized TLS with Gene Expression Profile in Glioma Samples

Group 21

Sai Renuka Chandrasekaran
Ole Lennart Decker
Raquel Ruiz Pascual
Parastoo Tabibzadehtehrani
Magdalena Zydorczak

Introduction

Study: “Spatial immune profiling defines a subset of human gliomas with functional tertiary lymphoid structures”[1]

Background:

  • Adult-type diffuse gliomas: “cold” tumors

  • Tertiary Lymphoid structures (TLSs)

Objectives:

  • Visualizing transcriptional patterns

  • Identifying key genes

  • Confirming TLS functionality

[1] Cakmak et al., 2025 – https://doi.org/10.1016/j.immuni.2025.09.018

Materials:

Dataset: GEO (NCBI) - GSE271059[2]

  • Metadata:

    • IDH_status

    • TLS_status

    • Localisation

    • Primary_or_Recurrent

    • Age

    • Gender

  • Raw data: gene expression count matrix (genes × samples)



[2] GEO series GSE271059 – https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271059

Methods

[3] Ayers et al., 2017 – https://pubmed.ncbi.nlm.nih.gov/28650338/

Results

Impact of TLS status on transcriptional signatures

  • TLS status does not strongly separate the samples

  • TLS group contains highly distinct outliers

Results

Impact of IDH status on transcriptional signatures

  • Clear and distinct separation of samples by IDH status

  • Outlier subsets exist

Results

TLS effect on gene expression

  • Each point = one gene (TLS vs control)
  • Several genes are significantly associated with TLS status (q < 0.05)
  • A large amount of genes are higher in TLS, others in controls

Results

GEP score by TLS status

  • Each point = one tumor sample
  • GEP score (18-gene immune signature)
  • Median GEP score is higher in TLS tumors than in controls

Results

Three genes were found to be TLS-significant

Discussion

  • PCA: TLS and control tumours largely overlap → TLS is not the main driver of variation.
  • PCA: clear separation by IDH status → strong impact of IDH mutations on expression.
  • PC1 + PC2 explain ~20% of variance in a >40,000-dimensional dataset.
  • TLS gliomas show widespread transcriptional differences compared with controls.
  • TLS samples have higher immune GEP scores → more active adaptive immune responses.
  • Only 3 GEP genes are individually significant → Coordinated moderate changes rather than strong effects per gene
  • Overall, TLS gliomas have a more active immune microenvironment; GEP scores could potentially help predict response to immunotherapy
  • Limitations: limited sample size, unaccounted confounders (e.g. sex, IDH), and simple linear models